A data-driven approach for discovering heat load patterns in district heating

TL;DR

This paper introduces a scalable, data-driven method for analyzing heat load patterns across large district heating networks, enabling better understanding and management of customer heat usage without prior knowledge.

Contribution

It presents a novel large-scale clustering and anomaly detection approach for district heating customer profiles, filling the gap of limited existing large-scale analyses.

Findings

Effective clustering of customer heat load profiles.

Identification of unusual customers with deviating patterns.

Demonstrated applicability on a large dataset of 1222 buildings.

Abstract

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers' heat-use habits.